SESSION TITLE: Critical Care Posters III
SESSION TYPE: Original Investigation Poster
PRESENTED ON: Wednesday, October 28, 2015 at 01:30 PM - 02:30 PM
PURPOSE: Risk stratification with the modified early warning score (MEWS) or electronic cardiac arrest trigger (eCART) has been utilized with ward patients as a manner of preemptively identifying high-risk patients who might benefit from enhanced monitoring including early intensive care unit (ICU) transfer. In-hospital mortality from cardiac arrest is approximately 80% making effective preventative interventions an important focus area. Critical care areas of the hospital typically have much lower patient to nurse ratios than the wards. As a consequence, there has been less emphasis on the development of ICU early warning systems. However it is well known that early identification and treatment of organ failure in the ICU improves outcomes. Our institution developed an early warning dashboard (EWD) identifying patients who may benefit from early interventions. The purpose of this study is to validate the individual data elements which compose the EWD algorithms.
METHODS: Using the adverse outcomes of cardiac arrest, ICU mortality and ICU readmissions, a retrospective case control study was performed using three demographic items (age, diabetes, and morbid obesity) and twenty-four EWD measured parameters which included vital signs, laboratory values, ventilator information, and other clinical information to validate the EWD. Odds ratios and one-tiered p-values were calculated.
RESULTS: Ten statistically significant areas were identified for cardiac arrest and twelve for ICU death. Significant parameters included heart rate > 120, new dialysis, oliguria, creatinine >2 or increase > 0.5, pressors at time of event, PaO2/FiO2<300, leukocytosis > 12000 cells/mL, bicarbonate<15 or decrease by 4 and lactate> 2.5 mmol/L. The ICU readmission outcome was compared to controls from both ICU patients and ward patients with statistical significance identified for one and two parameters respectively.
CONCLUSIONS: Several individual data elements achieved statistical significance and are currently incorporated into the next generation of more advanced clinical decision algorithms. The next step in the evaluation of the EWD will be a prospective study against historical controls.
CLINICAL IMPLICATIONS: This technology may have the ability to improve patient outcomes through the earlier identification of in-house patients at risk of deterioration. Additionally, the EWD could extend critical care expertise through large-scale patient monitoring of smaller hospitals constrained by limited intensivist staffing.
DISCLOSURE: The following authors have nothing to disclose: Michael Kavanaugh, Peter park, Konrad Davis
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